Neuromorphic Readout for Hadron Calorimeters
This work addresses signal processing challenges in particle physics experiments, offering a potentially fast and energy-efficient solution, though it appears incremental as it builds on existing neuromorphic and calorimeter concepts.
The paper tackled the problem of processing light signals from hadron calorimeters by simulating a neuromorphic computing system that encodes temporal photon distributions as spike trains to estimate deposited energy and spatial distribution, achieving valuable topological information without segmenting the active medium.
We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.